文章摘要

李广建,罗立群.走向知识融合——大数据环境下情报学的发展趋势[J].中国图书馆学报,2020,46(6):26~40
走向知识融合——大数据环境下情报学的发展趋势
Towards Knowledge Fusion:The Development Trend of Information Science in Big Data Environment
投稿时间:2020-04-13  
DOI:
中文关键词: 情报学  知识融合  情报分析  信息分析  大数据
英文关键词: Information science  Knowledge fusion  Intelligence analysis  Information analysis  Big data
基金项目:本文系国家社会科学基金重大项目“大数据时代知识融合的体系架构、实现模式及实证研究”(编号:15ZDB129)的研究成果之一
作者单位
李广建 北京大学信息管理系 北京 100871 
罗立群 北京大学信息管理系 北京 100871 
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中文摘要:
      本文首先对当前大数据环境下情报学研究与实践中知识融合的现状和趋势进行了总结,分别从情报理念、情报采集、情报分析、情报服务四个方面梳理了情报学研究与实践中有关知识融合应用的特点。其次概述现有知识科学领域中知识融合研究与实践的内容,并按主要研究内容和特征将其划分为三个发展阶段,分别为基于代理的知识融合、基于模式的知识融合、基于机器学习的知识融合。最后,结合近年来的情报实践,提出了一个知识融合研究的总体框架,一方面对现有情报学研究和实践进行概括和总结,反映当前情报学的新进展、新特征,另一方面勾画出未来情报学的一种发展路径,从而推动情报学的发展。图1。表1。参考文献 58。
英文摘要:
In recent years,an important development trend of the intelligence community is to emphasize the integration and consilience of different sources and types of intelligence In this process,knowledge fusion plays a key role,which has attracted the attention of researchers and practitioners of information science and related disciplines.
This article first summarizes the current situation and trends of knowledge fusion in the research and practice of information science under the current big data environment:1)The concept of intelligence has shifted from assisted decision support to direct prediction and early warning In the process of warning and early warning,knowledge fusion is indispensable. 2)Intelligence gathering has shifted from traditional task oriented passive gathering to active perception of content-based understanding In the big data environment,with the development of data processing technology and intelligent algorithm,the method of information collection has transformed from traditional task-oriented collection to more intelligent intelligence perception. 3)Intelligence analysis has further expanded from focusing on association relationships to the exploration of causality In the big data environment,the demand for causal analysis in the intelligence field is increasingly strong,and it is gradually becoming the “normal” of intelligence analysis. 4)Intelligence services have changed from knowledge services to wisdom services On the one hand,the application of intelligent information technology is the core of the smart service,which is different from the application of information technology in the past. On the other hand,smart intelligence services not only need to have a comprehensive and global perception of the intelligence environment but also have to deeply understand the intelligence environment issues by merging background knowledge and tacit knowledge of related issues.
Secondly,the article summarizes the existing knowledge fusion research and practice in the field of knowledge science,and divides it into three development stages according to the main research content and characteristics:agent based knowledge fusion,pattern based knowledge fusion,and machine learning based knowledge fusion Agent based knowledge fusion is an early representative of knowledge fusion research Its core task is to solve the problem of knowledge sharing,reuse and transformation in a distributed information environment,and to implement knowledge search and extraction of many knowledge resources through middleware technology. Pattern based knowledge fusion mainly realizes the fusion of knowledge by changing and reorganizing the internal and external structure and attributes of knowledge driven by multi-source ontology Its main goal is to solve the problem of contextual situation awareness and decision-making in complex scenarios. Machine learning based knowledge fusion mainly uses automatic methods such as machine learning to achieve automatic knowledge extraction and learning of open data,the establishment of knowledge links,and the unification of knowledge Finally,automatic construction of large-scale knowledge bases and automatic organization of knowledge are achieved.
Finally,combined with the intelligence practice in recent years,this paper proposes a general framework for knowledge fusion research,including thought domain,theory domain,technology domain,and application domain The thought domain is our set of guiding thought for dealing with complex,changeable,and deeply uncertain intelligence environments It is used to guide and lead intelligence research and intelligence work ideologically and theoretically The thought domain of knowledge fusion in the field of intelligence is composed of fusion thinking,intelligence thinking,and computational thinking,and the three kinds of thinking complement each other The main basic or source disciplines of these three kinds of thinking are cognitive science,information science,and computational science,respectively The theory domain of knowledge fusion mainly includes four aspects:knowledge fusion theory,knowledge fusion framework,knowledge fusion model,and knowledge fusion method Technology domain refers to different types of intelligence and cognitive activities Knowledge fusion models,methods,frameworks,software and other technical elements are different In this way,a set of technical elements oriented to a specific task or solving a type of problem forms different technologies field. The current application fields of knowledge fusion include financial intelligence,public safety,business analysis,military intelligence,scientific forecasting,and public opinion management We believe that with the continuous development of technology,the application space of knowledge fusion will become wider and wider1 fig1 tab58 refs.
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